genetic_correlations

Author

Brian M. Schilder

knitr::opts_chunk$set(echo = TRUE, root.dir = here::here())

library(dplyr)
library(ggplot2)
library(googledrive)
library(data.table)
set.seed(2019)

Meta-analysis of genetic correlation studies between Parkinson’s Disease and other phenotypes.

Prepare data

library(phenomix)

## API: public: http://gwas-api.mrcieu.ac.uk/

## Registered S3 method overwritten by 'spatstat.geom':
##   method     from
##   print.boxx cli

data("DEGAS_seurat")

pd_traits <- DEGAS_seurat@meta.data[grepl("parkinson",DEGAS_seurat@meta.data$label_phe, ignore.case = TRUE),]

trait_key <- setNames(DEGAS_seurat@meta.data$label_phe,
                      DEGAS_seurat@meta.data$label_phe_code)

degas_corr <- cor(Matrix::t(DEGAS_seurat@reductions$contributionGene@cell.embeddings))[,rownames(pd_traits)] %>%
  data.table::data.table(keep.rownames = "Trait2_code")
degas_corr[,Trait2:=trait_key[Trait2_code],] 
 
degas_corr <- melt(degas_corr, 
                   id.vars=c("Trait2_code","Trait2"), 
                   variable.name = "Trait1_code", 
                   value.name = "corr",
                   #### Important! otherwise will screw up dict #@##
                   variable.factor = FALSE)
degas_corr[,Trait1:=trait_key[Trait1_code],]


data.table::setorder(degas_corr, -corr)
data.table::setcolorder(degas_corr,
                        c("Trait1","Trait2",
                          "Trait1_code","Trait2_code","corr"))

# data.table::fwrite(degas_corr,
                # here::here("data/DEGAS/DEGAS_contributionGene_corr.csv"))

Download

googledrive::drive_download("https://docs.google.com/spreadsheets/d/19jz9l2P7W2f1PWT9t3x0L8h6VQL021RdRI7D4FAOjX0/edit?usp=sharing#gid=1366974120", path = here::here("data/metaanalysis/TableS2.xlsx"), overwrite = T)

## ! Using an auto-discovered, cached token.

##   To suppress this message, modify your code or options to clearly consent to
##   the use of a cached token.

##   See gargle's "Non-interactive auth" vignette for more details:

##   <https://gargle.r-lib.org/articles/non-interactive-auth.html>

## ℹ The googledrive package is using a cached token for
##   'brian_schilder@alumni.brown.edu'.

## File downloaded:

## • 'TableS2' <id: 19jz9l2P7W2f1PWT9t3x0L8h6VQL021RdRI7D4FAOjX0>

## Saved locally as:

## • '/Desktop/PD_omics_review/data/metaanalysis/TableS2.xlsx'

sheets <- readxl::excel_sheets(here::here("data/metaanalysis/TableS2.xlsx")) 

Merge datasets

corr_sheets <- sheets[endsWith(sheets,"_corr")]
 
corr_data <- lapply(corr_sheets, function(sheet){
  print(sheet)
  dat <- readxl::read_excel(here::here("data/metaanalysis/TableS2.xlsx"), sheet = sheet) 
  dat$Sheet <- gsub("_corr","",sheet) 
  cols <- c("Trait1","Trait2","corr",
            "Reference","Source","Dataset")  
  cols <- cols[cols %in% colnames(dat)]
  return(dplyr::relocate(dat, all_of(cols), .before = 1))
}) %>% `names<-`(corr_sheets) %>%
  data.table::rbindlist(fill = TRUE)

## [1] "Bryois2020_corr"
## [1] "Nalls2019_corr"
## [1] "Agarwal2020_corr"
## [1] "DeGAs_corr"
## [1] "dPRS_corr"

Harmonise names

Attempt to somewhat harmonise phenotype names across studies.

corr_data <- corr_data %>% 
  dplyr::mutate(Phenotype = stringr::str_trunc(stringr::str_wrap(tolower(Trait2), 50),50)
) 

Top traits

Get the top N traits per study that are most highly correlated with Parkinson’s Disease.

Gather

max_traits <- 10
  
top_corr <- corr_data %>% 
  subset(Trait1!=Trait2) %>%
  dplyr::group_by(Sheet, Trait1) %>%
  dplyr::mutate(Phenotype = paste0(Phenotype,"@",group_indices())) %>%
  dplyr::slice_max(order_by = corr, n = max_traits) %>%
  dplyr::select(Sheet, Trait1, Trait2, Phenotype, corr, Source) %>% 
  dplyr::ungroup() %>%
  dplyr::arrange(corr)

## Warning: `group_indices()` was deprecated in dplyr 1.0.0.
## Please use `cur_group_id()` instead.

top_corr$Phenotype <- factor(top_corr$Phenotype, 
                          unique(top_corr$Phenotype),
                          ordered = TRUE)
knitr::kable(top_corr)

Sheet	Trait1	Trait2	Phenotype	corr	Source
Bryois2020	Parkinson’s disease	Stroke	stroke@2	0.0197000	Supplementary Table 4
Bryois2020	Parkinson’s disease	Bipolar	bipolar@2	0.0244000	Supplementary Table 4
Bryois2020	Parkinson’s disease	Waist to hip ratio adjusted for BMI	waist to hip ratio adjusted for bmi@2	0.0325000	Supplementary Table 4
Bryois2020	Parkinson’s disease	Intelligence	intelligence@2	0.0448000	Supplementary Table 4
Bryois2020	Parkinson’s disease	Educational attainment	educational attainment@2	0.0460000	Supplementary Table 4
Bryois2020	Parkinson’s disease	Hippocampal volume	hippocampal volume@2	0.0573000	Supplementary Table 4
Bryois2020	Parkinson’s disease	Alzheimer’s disease	alzheimer’s disease@2	0.0776000	Supplementary Table 4
Bryois2020	Parkinson’s disease	Anorexia	anorexia@2	0.0900000	Supplementary Table 4
Bryois2020	Parkinson’s disease	Amyotrophic lateral sclerosis	amyotrophic lateral sclerosis@2	0.1884000	Supplementary Table 4
Nalls2019	Parkinson’s Disease	Mean Pallidum	mean pallidum@5	0.2332000	Supplementary Table 10
Nalls2019	Parkinson’s Disease	Number of older siblings	number of older siblings@5	0.2470000	Supplementary Table 10
Nalls2019	Parkinson’s Disease	Mean Putamen	mean putamen@5	0.2481000	Supplementary Table 10
Nalls2019	Parkinson’s Disease	Diagnoses - main ICD10: C61 Malignant neoplasm of prostate	diagnoses - main icd10: c61 malignant neoplasm …@5	0.2512000	Supplementary Table 10
Nalls2019	Parkinson’s Disease	GCSF.sumstats.gz	gcsf.sumstats.gz@5	0.2636000	Supplementary Table 10
Nalls2019	Parkinson’s Disease	Underlying (primary) cause of death: ICD10: J84.1 Other interstitial pulmonary diseases with fibrosis	underlying (primary) cause of death: icd10:
j84…@5	0.2696000	Supplementary Table 10
Nalls2019	Parkinson’s Disease	Mean Accumbens	mean accumbens@5	0.2770000	Supplementary Table 10
Bryois2020	Parkinson’s disease	Intracranial volume	intracranial volume@2	0.2851000	Supplementary Table 4
Agarwal2020	Parkinson disease	Coronary Artery Disease	coronary artery disease@1	0.2908336	Computed by review authors
Nalls2019	Parkinson’s Disease	Cancer code_ self-reported: small intestine/small bowel cancer	cancer code_ self-reported: small intestine/sma…@5	0.2971000	Supplementary Table 10
Agarwal2020	Parkinson disease	Ulcerative colitis	ulcerative colitis@1	0.3022025	Computed by review authors
Nalls2019	Parkinson’s Disease	ICV	icv@5	0.3511000	Supplementary Table 10
Agarwal2020	Parkinson disease	Anxiety	anxiety@1	0.3739806	Computed by review authors
Nalls2019	Parkinson’s Disease	Diagnoses - main ICD10: B37 Candidiasis	diagnoses - main icd10: b37 candidiasis@5	0.3822000	Supplementary Table 10
Agarwal2020	Parkinson disease	ADHD	adhd@1	0.3851822	Computed by review authors
Agarwal2020	Parkinson disease	Schizophrenia (2018)	schizophrenia (2018)@1	0.4534542	Computed by review authors
Agarwal2020	Parkinson disease	Intelligence	intelligence@1	0.4798319	Computed by review authors
DeGAs	Parkinson’s disease	helicobacter pylori	helicobacter pylori@3	0.5041095	Computed by review authors
DeGAs	Parkinson’s disease	gestational hypertension/pre-eclampsia	gestational hypertension/pre-eclampsia@3	0.5083302	Computed by review authors
DeGAs	Parkinson’s disease	Age hayfever or allergic rhinitis diagnosed by doctor	age hayfever or allergic rhinitis diagnosed by
…@3	0.5105433	Computed by review authors
DeGAs	Parkinson’s disease	Number of children fathered	number of children fathered@3	0.5118207	Computed by review authors
DeGAs	Parkinson’s disease	chickenpox	chickenpox@3	0.5452407	Computed by review authors
DeGAs	Parkinson’s disease	metoclopramide	metoclopramide@3	0.5535843	Computed by review authors
DeGAs	Parkinson’s disease	Mean ISOVF in fornix cres+stria terminalis on FA skeleton (left)	mean isovf in fornix cres+stria terminalis on f…@3	0.5675078	Computed by review authors
DeGAs	Parkinson’s disease	Mean ISOVF in fornix cres+stria terminalis on FA skeleton (right)	mean isovf in fornix cres+stria terminalis on f…@3	0.5870492	Computed by review authors
DeGAs	Parkinson’s disease	pulmonary fibrosis	pulmonary fibrosis@3	0.5958769	Computed by review authors
Agarwal2020	Parkinson disease	BMI	bmi@1	0.6242659	Computed by review authors
Agarwal2020	Parkinson disease	Tourette Syndrome	tourette syndrome@1	0.6766420	Computed by review authors
DeGAs	Parkinson’s disease	anal fissure	anal fissure@3	0.6783448	Computed by review authors
Agarwal2020	Parkinson disease	Amyotrophic Lateral Sclerosis	amyotrophic lateral sclerosis@1	0.7070386	Computed by review authors
Agarwal2020	Parkinson disease	Epilepsy (General)	epilepsy (general)@1	0.7243838	Computed by review authors
DeGAs	parkinsons disease	connective tissue disorder	connective tissue disorder@4	0.8339059	Computed by review authors
DeGAs	parkinsons disease	movicol oral powder	movicol oral powder@4	0.8404283	Computed by review authors
DeGAs	parkinsons disease	fracture pelvis	fracture pelvis@4	0.8412213	Computed by review authors
DeGAs	parkinsons disease	fracture finger	fracture finger@4	0.8427052	Computed by review authors
DeGAs	parkinsons disease	finasteride	finasteride@4	0.8441802	Computed by review authors
DeGAs	parkinsons disease	bile duct obstruction/ascending cholangitis	bile duct obstruction/ascending cholangitis@4	0.8465642	Computed by review authors
DeGAs	parkinsons disease	sclerosing cholangitis	sclerosing cholangitis@4	0.8489558	Computed by review authors
DeGAs	parkinsons disease	pneumothorax	pneumothorax@4	0.8494077	Computed by review authors
DeGAs	parkinsons disease	alcoholic liver disease/alcoholic cirrhosis	alcoholic liver disease/alcoholic cirrhosis@4	0.8557067	Computed by review authors
DeGAs	parkinsons disease	fracture patella/knee	fracture patella/knee@4	0.8606616	Computed by review authors

Plot

corr_plot <- ggplot(top_corr, 
                    aes(x=corr, y=Phenotype, fill=Sheet)) +
  facet_grid(facets = Sheet ~., 
             scales = "free_y") + 
  geom_bar(stat="identity", aes(alpha=corr), show.legend = F) +
  labs(title = "Genetic correlations with PD",
       x="Correlation", y="Phenotype") +
  scale_fill_manual(values=pals::gnuplot(dplyr::n_distinct(top_corr$Sheet)+1 ) ) +
  scale_x_continuous(limits = c(0,1)) +
  # Create a dictionary mapping the new labels
  scale_y_discrete(labels = setNames(gsub("@.*","",levels(top_corr$Phenotype)),
                                     levels(top_corr$Phenotype) ) ) + 
  theme_bw() +
  theme(strip.background = element_rect(fill = "black"), 
        strip.text = element_text(color = "white", angle = 0),
        strip.text.y = element_text(color = "white", angle = 0),
        panel.grid.minor.x = element_blank(),
        panel.grid.minor.y = element_blank())
print(corr_plot)

## Warning: Removed 1 rows containing missing values (geom_bar).

ggsave(here::here("plots/metaanalysis/correlation_metaanalysis.pdf"),corr_plot, dpi = 300, height = 15)

## Saving 7 x 15 in image

## Warning: Removed 1 rows containing missing values (geom_bar).

Bryois 2020

bryois <- readxl::read_excel(here::here("data/metaanalysis/TableS2.xlsx"), sheet="Bryois2020_corr")

mat_bryois <- as.matrix(bryois$rg) %>% `rownames<-`(bryois$Trait2) %>% `colnames<-`("rg")

heat <- ggplot(bryois, aes(y=Trait2, x=1, fill=rg)) +
  geom_tile() + 
  theme_bw()
plotly::ggplotly(heat)

LD Hub

I began to explore this dataset but then realized it does not contain any Parkinson’s Disease GWAS.

rg = xlsx::read.xlsx(here::here("data/LD_Hub/LD-Hub_genetic_correlation_221x221_no_ENIGMA.xlsx"), sheetName = "rG") %>% 
  tibble::column_to_rownames("NA.")
colnames(rg) <- gsub("[.]","-",colnames(rg))
row.names(rg) <- gsub("[.]","-",row.names(rg))
rg[rg=="/"] <- NA 
rg <- Matrix::as.matrix(rg, sparse = T)


rp = xlsx::read.xlsx(here::here("data/LD-Hub_genetic_correlation_221x221_no_ENIGMA.xlsx"), sheetName = "rP")%>%
    tibble::column_to_rownames("NA.")
colnames(rp) <- gsub("[.]","-",colnames(rp))
row.names(rp) <- gsub("[.]","-",row.names(rp))
rp[rp=="/"] <- NA 
rp <- Matrix::as.matrix(rp, sparse = T)

Check for Parkinson’s

grep("Parkinson",colnames(rp), ignore.case = T, value = T)

Session Info

utils::sessionInfo()

## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=C             
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] phenomix_0.2.0    data.table_1.14.0 googledrive_2.0.0 ggplot2_3.3.5    
## [5] dplyr_1.0.7      
## 
## loaded via a namespace (and not attached):
##   [1] utf8_1.2.2                  reticulate_1.20            
##   [3] R.utils_2.10.1              lme4_1.1-27.1              
##   [5] tidyselect_1.1.1            RSQLite_2.2.7              
##   [7] AnnotationDbi_1.55.1        htmlwidgets_1.5.3          
##   [9] grid_4.1.0                  BiocParallel_1.27.3        
##  [11] Rtsne_0.15                  munsell_0.5.0              
##  [13] ragg_1.1.3                  codetools_0.2-18           
##  [15] ica_1.0-2                   future_1.21.0              
##  [17] miniUI_0.1.1.1              withr_2.4.2                
##  [19] colorspace_2.0-2            Biobase_2.53.0             
##  [21] filelock_1.0.2              highr_0.9                  
##  [23] knitr_1.33                  rstudioapi_0.13            
##  [25] Seurat_4.0.3                stats4_4.1.0               
##  [27] ROCR_1.0-11                 tensor_1.5                 
##  [29] listenv_0.8.0               labeling_0.4.2             
##  [31] MatrixGenerics_1.5.3        GenomeInfoDbData_1.2.6     
##  [33] polyclip_1.10-0             farver_2.1.0               
##  [35] bit64_4.0.5                 rprojroot_2.0.2            
##  [37] parallelly_1.27.0           vctrs_0.3.8                
##  [39] generics_0.1.0              xfun_0.25                  
##  [41] BiocFileCache_2.1.1         doParallel_1.0.16          
##  [43] R6_2.5.0                    GenomeInfoDb_1.29.3        
##  [45] pals_1.7                    bitops_1.0-7               
##  [47] spatstat.utils_2.2-0        cachem_1.0.5               
##  [49] DelayedArray_0.19.1         assertthat_0.2.1           
##  [51] promises_1.2.0.1            BiocIO_1.3.0               
##  [53] scales_1.1.1                gtable_0.3.0               
##  [55] globals_0.14.0              goftest_1.2-2              
##  [57] rlang_0.4.11                systemfonts_1.0.2          
##  [59] MungeSumstats_1.1.14        splines_4.1.0              
##  [61] rtracklayer_1.53.1          lazyeval_0.2.2             
##  [63] gargle_1.2.0                dichromat_2.0-0            
##  [65] broom_0.7.9                 spatstat.geom_2.2-2        
##  [67] abind_1.4-5                 yaml_2.2.1                 
##  [69] reshape2_1.4.4              crosstalk_1.1.1            
##  [71] backports_1.2.1             GenomicFeatures_1.45.1     
##  [73] httpuv_1.6.1                tools_4.1.0                
##  [75] ellipsis_0.3.2              gplots_3.1.1               
##  [77] spatstat.core_2.3-0         jquerylib_0.1.4            
##  [79] RColorBrewer_1.1-2          BiocGenerics_0.39.1        
##  [81] ggridges_0.5.3              Rcpp_1.0.7                 
##  [83] plyr_1.8.6                  progress_1.2.2             
##  [85] zlibbioc_1.39.0             purrr_0.3.4                
##  [87] RCurl_1.98-1.4              prettyunits_1.1.1          
##  [89] openssl_1.4.4               rpart_4.1-15               
##  [91] deldir_0.2-10               pbapply_1.4-3              
##  [93] cowplot_1.1.1               S4Vectors_0.31.0           
##  [95] zoo_1.8-9                   SeuratObject_4.0.2         
##  [97] SummarizedExperiment_1.23.1 ggrepel_0.9.1              
##  [99] cluster_2.1.2               fs_1.5.0                   
## [101] here_1.0.1                  variancePartition_1.23.1   
## [103] magrittr_2.0.1              scattermore_0.7            
## [105] lmtest_0.9-38               RANN_2.6.1                 
## [107] fitdistrplus_1.1-5          matrixStats_0.60.0         
## [109] hms_1.1.0                   patchwork_1.1.1            
## [111] mime_0.11                   evaluate_0.14              
## [113] xtable_1.8-4                pbkrtest_0.5.1             
## [115] XML_3.99-0.7                readxl_1.3.1               
## [117] sparsesvd_0.2               IRanges_2.27.0             
## [119] gridExtra_2.3               compiler_4.1.0             
## [121] biomaRt_2.49.4              maps_3.3.0                 
## [123] tibble_3.1.3                KernSmooth_2.23-20         
## [125] crayon_1.4.1                minqa_1.2.4                
## [127] R.oo_1.24.0                 htmltools_0.5.1.1          
## [129] mgcv_1.8-36                 later_1.2.0                
## [131] tidyr_1.1.3                 DBI_1.1.1                  
## [133] gprofiler2_0.2.0            dbplyr_2.1.1               
## [135] MASS_7.3-54                 rappdirs_0.3.3             
## [137] boot_1.3-28                 Matrix_1.3-4               
## [139] cli_3.0.1                   R.methodsS3_1.8.1          
## [141] parallel_4.1.0              igraph_1.2.6               
## [143] GenomicRanges_1.45.0        pkgconfig_2.0.3            
## [145] GenomicAlignments_1.29.0    plotly_4.9.4.9000          
## [147] spatstat.sparse_2.0-0       foreach_1.5.1              
## [149] xml2_1.3.2                  bslib_0.2.5.1              
## [151] XVector_0.33.0              GeneOverlap_1.29.0         
## [153] stringr_1.4.0               VariantAnnotation_1.39.0   
## [155] digest_0.6.27               sctransform_0.3.2          
## [157] RcppAnnoy_0.0.19            spatstat.data_2.1-0        
## [159] Biostrings_2.61.2           cellranger_1.1.0           
## [161] rmarkdown_2.10              leiden_0.3.9               
## [163] uwot_0.1.10.9000            restfulr_0.0.13            
## [165] curl_4.3.2                  shiny_1.6.0                
## [167] Rsamtools_2.9.1             gtools_3.9.2               
## [169] nloptr_1.2.2.2              rjson_0.2.20               
## [171] nlme_3.1-152                lifecycle_1.0.0            
## [173] jsonlite_1.7.2              mapproj_1.2.7              
## [175] askpass_1.1                 limma_3.49.4               
## [177] viridisLite_0.4.0           BSgenome_1.61.0            
## [179] fansi_0.5.0                 pillar_1.6.2               
## [181] lattice_0.20-44             KEGGREST_1.33.0            
## [183] fastmap_1.1.0               httr_1.4.2                 
## [185] survival_3.2-12             googleAuthR_1.4.0          
## [187] glue_1.4.2                  iterators_1.0.13           
## [189] png_0.1-7                   bit_4.0.4                  
## [191] stringi_1.7.3               sass_0.4.0                 
## [193] blob_1.2.2                  textshaping_0.3.5          
## [195] caTools_1.18.2              memoise_2.0.0              
## [197] irlba_2.3.3                 future.apply_1.8.1